Discovering and presenting décor harmonized with a décor style

ABSTRACT

Technology is disclosed for discovering décor harmonized with a décor style (“the technology”). The décor includes décor items, e.g. artworks, paintings, pictures, artifacts, architectural pieces, arrangement of artworks, color selection, room décor, rugs, mats, furnishings, household items, fashion, clothes, jewelry, car interiors, garden arrangements etc. The technology facilitates analyzing user input to identify a décor style from a décor style dictionary, obtaining décor that harmonizes with décor style, and presenting a representation of the décor to the user. The décor style dictionary includes décor styles that are generated based on an analysis of content, including images and description of décor, from a plurality of sources. The décor styles can be based on a number of concepts, including a theme of the décor, a color/color palette, a mood of the person, a fashion era, a type of architecture, etc. The technology facilitates presentation of discovered décor using computer generated imagery techniques.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional Application Ser.Nos. 61/809,802 titled “DIGITAL ART SYSTEMS AND METHODS” filed Apr. 8,2013; 61/809,832 titled “DISCOVERING, VISUALIZING AND FACILITATING THESELECTION OF ART, DESIGN, AND DECOR” filed Apr. 8, 2013; and 61/824,967titled “DISCOVERING, VISUALIZING AND FACILITATING THE SELECTION OF ART,DESIGN, AND DECOR” filed May 17, 2013, all of which are incorporatedherein by reference for all purposes in their entirety.

TECHNICAL FIELD

The disclosure relates to online tools for discovering décor, and morespecifically to discovering and presenting décor that is harmonized witha décor style.

BACKGROUND

Current tools for finding décor items such as art, rugs, decoratingpaint, furnishings, fashion etc. are typically naïve. They do notaddress the problem of finding different décor items that match orharmonize to a particular required style. The current tools lackabilities to select décor items with visually common features, such ascolor or style. The current tools lack abilities to search across theWeb for décor items based on décor attributes, such as color, the moodof a person, etc. Further, the décor styles are expressed by varioususers in various ways. For example, there are number of colors called“Green,” the style “minimal” may mean differently to different users.The current tools typically lack abilities to co-ordinate thesedifferent ways of expressing a décor style to find harmonized décoritems.

SUMMARY

Technology is disclosed for discovering décor that is harmonized with adécor style (“the technology”). The décor can include one or more of avariety of décor items such as artworks, paintings, pictures, artifacts,architectural pieces, arrangement of artworks, color selection, décor ofa room, rugs, mats, furnishings, clothes, jewelry, fashion, carinteriors, flower arrangements, gardens, etc. In various embodiments,the décor styles are defined based on a number of concepts including atheme of the décor such as “minimal,” “abstract,” “calm”; a mood of theperson such as “happy,” “party,” “romantic”; a type of architecture suchas “traditional” “contemporary,” “Victorian”; color themes such as“Moroccan,” “Greece” etc.

The technology facilitates analyzing a user input to determine a décorstyle specified by the user, identifying a décor style classifier from asystem generated décor style classifier dictionary containing a numberof décor styles that can be used by a décor discovery system fordiscovering décor, determining various décor based on the décor styleclassifier, and presenting a representation of the determined décor tothe user using a variety of presentation techniques.

A particular décor style is characterized by style data which caninclude keywords or phrases that are indicative or descriptive of theparticular style; sample images of the décor that match the particularstyle; and features of the décor items that are indicative of theparticular style, which can include features such as a number of décoritems in the room, placement of the décor items, color pattern of theroom, color of the décor items, design of the décor items (e.g. form,shape, materials) etc. In various embodiments such features of the décorstyle can be gathered using morphological analysis techniques on animage representative of a décor of the particular décor style. Onemethod of generating such style data is by crawling the World Wide Weband obtaining the data from websites, blogs, articles etc. discussinginterior decoration, art, décor, etc. The style data can also beobtained from a number of other sources such as online magazines,documents that discuss fashion, interior decoration, art, décor,preferences and tastes of various users etc.

In some embodiments, the style data for a particular décor style may bedefined by the user. The user can be an end user who consumes or isseeking recommendations regarding décor items, an expert (in décor) suchas an architect, an interior designer, a photo stylist, or a clothingdesigner. The décor discovery system can also track trends in taste anddécor style by tracking data associated with influential sources (e.g.fashion designers) in the décor industry. The décor discovery systemcreates, manages and updates the décor style dictionary with the styledata obtained using the above methods.

In various embodiments, the décor discovery system tracks userpreferences for a particular décor style based on the various décoritems chosen by the user and uses the style data associated with userpreference to update the décor style dictionary as necessary.

In various embodiments, the décor discovery system facilitates automaticgeneration of color palettes based on a number of décor styles such as amood of a person, travel theme, fashion era etc. The suggested colorpalettes may be then used, for example, in creation of art, selection ofart that matches with the décor of a room, selection of a new décor forthe room, etc.

In various embodiments, the décor discovery system may discover décorbased on a décor style data input by the user. The user may specify therequired décor style in the form of keywords and/or images. The keywordscan be indicative of style names, features or attributes, color patternetc. of the décor items. The image can be of one or more of décor items.For example, the user may upload a picture of an existing room to thedécor discovery system and discover décor, e.g. decor items that areharmonized with the décor of the room.

In various embodiments, the décor discovery system facilitatespresentation of the discovered décor using various presentationtechniques such as three dimensional (3D) computer generated imagery(CGI) modeling. The user may view the discovered décor, for example,décor of a room or arrangement of a garden generated using 3D CGItechniques.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an environment in which thetechnology can operate in various embodiments.

FIG. 2 is a block diagram illustrating a system to create a dictionaryof décor styles, consistent with various embodiments.

FIG. 3 is an example implementation of a décor style dictionary,consistent with various embodiments.

FIG. 4 is an example of images of various décor styles, consistent withvarious embodiments.

FIG. 5 is an example of a tool for generating a color palette based ondécor style, consistent with various embodiments.

FIG. 6 is a block diagram of a process for discovering décor based ondécor styles, consistent with various embodiments.

FIG. 7 is an example of presentation of an artwork in a room using threedimensional (3D) computer generated imagery (CGI) modeling, consistentwith various embodiments.

FIG. 8 is an example of art descriptor presented with an artwork,consistent with various embodiments.

FIGS. 9A, 9B and 9C are collectively an example of arrangement ofartworks based on décor styles, consistent with various embodiments.

FIG. 10 is a flow diagram of a process for discovering décor based ondécor styles, consistent with various embodiments.

FIG. 11 is a flow diagram of a process for creating a décor styledictionary, consistent with various embodiments.

FIG. 12 is a flow diagram of a process for associating décor styles to adécor style classifier, consistent with various embodiments.

FIG. 13 is a flow diagram of a process for creating a décor of aparticular décor style, consistent with various embodiments.

FIG. 14 is a block diagram illustrating components of an apparatus thatmay perform various operations described by the technology.

DETAILED DESCRIPTION

Technology is disclosed for discovering décor that is harmonized with adécor style (“the technology”). The décor can include one or more of avariety of décor items such as artworks, paintings, pictures, artifacts,architectural pieces, audio (e.g. music), arrangement of artworks, colorselection, décor of a room, rugs, mats, furnishings, clothes, jewelry,fashion, car interiors, flower arrangements, gardens, etc. In variousembodiments, the décor styles are defined based on a number of conceptsincluding a theme of the décor such as “minimal,” “abstract,” “calm”; amood of the person such as “happy,” “party,” “romantic”; a type ofarchitecture such as “traditional” “contemporary,” “Victorian”; colorthemes such as “Moroccan,” “Greece” etc.

The technology facilitates analyzing a user input to determine a décorstyle specified by the user, identifying a décor style classifier from asystem generated décor style classifier dictionary containing a numberof décor styles that can be used by a décor discovery system fordiscovering décor, determining various décor based on the décor styleclassifier, and presenting a representation of the determined décor tothe user using a variety of presentation techniques.

A particular décor style is characterized by style data which caninclude keywords or phrases that are indicative or descriptive of theparticular style; sample images of the décor that match the particularstyle; and features of the décor items that are indicative of theparticular style, which can include features such as a number of décoritems in the room, placement of the décor items, color pattern of theroom, color of the décor items, design of the décor items (e.g. theirform, shape, materials) etc. In various embodiments such features of thedécor style can be gathered using morphological analysis techniques onan image representative of a décor of the particular décor style. Onemethod of generating such style data is by crawling the World Wide Weband obtaining the data from websites, blogs, articles etc. discussinginterior decoration, art, décor, etc. The style data can also beobtained from a number of other sources such as online magazines,documents that discuss fashion, interior decoration, art, décor,preferences and tastes of various users etc.

In some embodiments, the style data for a particular décor style may bedefined by the user. The user can be an end user who consumes or isseeking recommendations regarding décor items, an expert (in décor) suchas an architect, an interior designer, a photo stylist, or a jewelrydesigner. The décor discovery system can also track trends in taste anddécor style by tracking data associated with influential sources in thedécor industry. The décor discovery system creates, manages and updatesthe décor style dictionary with the style data obtained using the abovemethods.

In various embodiments, the décor discovery system tracks userpreferences for a particular décor style based on the various décoritems chosen by the user and uses the style data associated with userpreference to update the décor style dictionary as necessary.

In various embodiments, the décor discovery system facilitates automaticgeneration of color palettes based on a number of décor styles such as amood of a person, travel theme, fashion era etc. The suggested colorpalettes may be then used, for example, in creation of art, selection ofart that matches with the décor of a room, selection of a new décor forthe room, etc.

In various embodiments, the décor discovery system may discover décorbased on a décor style data input by the user. The user may specify therequired décor style in the form of keywords and/or images. The keywordscan be indicative of style names, features or attributes, color patternetc. of the décor items. The image can be of one or more of décor items.For example, the user may upload a picture of an existing room to thedécor discovery system and discover décor, e.g. décor items that areharmonized with the décor of the room.

In various embodiments, the décor discovery system facilitatespresentation of the discovered décor using various presentationtechniques such as three dimensional (3D) computer generated imagery(CGI) modeling. The user may view the discovered décor, for example,décor of a room or arrangement of a garden generated using 3D CGItechniques. The above mentioned embodiments and other embodiments of thetechnology are described below with reference to FIGS. 1-14.

Environment for Décor Discovery

Turning now to FIG. 1, FIG. 1 is a block diagram illustrating anenvironment 100 in which the technology can operate in variousembodiments. The décor discovery system 105 facilitates a user 110 todiscover décor, such as the décor items mentioned above, based on thestyle data 115 input by the user 110. The décor discovery system 105analyzes the style data 115 to determine a particular décor styleclassifier in a décor style dictionary (not illustrated) that defines adécor style corresponding to the style data 115. The décor discoverysystem 105 retrieves images of a number of décor items 125 based on theparticular décor style classifier and presents the images of décor items125 to the user 110. In some embodiments, the décor discovery system 105can combine various décor items into one harmonized décor and presentthe harmonized décor to the user. For example, the décor discoverysystem 105 can determine various décor items of a room based on aparticular décor style, combine the determined décor items to form aharmonized room décor, and present the harmonized room décor to theuser.

In some embodiments, the user 110 may want to view décor items of theparticular décor style having a certain combination of colors. The décordiscovery system 105 generates a number of color palettes 120 based onthe particular décor style classifier. The user 110 may choose one ofthe color palettes 120 and view only those décor items having colorcombinations similar to the chosen color palette 120.

In various embodiments, the style data 115 input by the user can includedata such as “minimal,” “abstract,” “calm,” “Zen-like” indicating atheme of the décor; “happy,” “party,” “romantic” indicating a mood ofthe person; “traditional” “contemporary,” “Victorian” indicating a typeof architecture; “Moroccan,” “Greece” indicating color themes etc. Invarious embodiments, the style data 115 can be input by the user 110 inthe form of free text, an image depicting a particular décor style, auser selection of predefined styles, or a combination of the foregoing.

In various embodiments, the accuracy of recommendation of various décoritems by the décor discovery system 105 depends on a definition aparticular décor style classifier to include an extensive set offeatures the users may associate with a décor style represented by theparticular décor style classifier, and a proper identification of aparticular décor style classifier from the décor style dictionary basedon the user input. The creation of décor style classifiers and the décorstyle dictionary are described in further detail with reference to atleast FIGS. 2-5.

Décor Style Dictionary

Turning now to FIG. 2, FIG. 2 is a block diagram illustrating a system200 to create a dictionary of décor styles, consistent with variousembodiments. In various embodiments, the system 200 may be implementedin the environment 100 of FIG. 1. The system 200 can be used to definevarious décor styles and organize them as corresponding décor styleclassifiers in a décor style dictionary 225. The décor style dictionary225 may be stored in computer readable medium according to any suitablestorage mechanism such as well known database storage techniques. Anexample implementation of a décor style dictionary created using system200 is illustrated in FIG. 3.

The example implementation 300 of the décor style dictionary includesdécor style classifiers such as “Romantic Style,” “Zen-like Style,”“Abstract.” Each of these décor style classifiers describe a particulardécor style. The factors that can contribute to defining, identifying,or describing the particular décor style is included in the signature ofthe particular décor style classifier. For example, when a user sayshe/she wants the décor of the room to be “romantic,” he/she may meanthat room has to have vases with red roses, a love seat, a light coloredwall, a number of paintings with subject as romance or love. Similarly,for another user “romantic” style may mean that the room has to haveflower themed chandeliers, large areas of pink color, smooth satinsheets, cushions with heart designs on them, etc. Accordingly, the décorstyle classifier “Romantic Style” is defined using an extensive set offeatures that the users may associate the style with. As illustrated inthe example implementation 300 of the décor style dictionary, thesignature of décor style classifier “Romantic Style” can include (a)keywords such as “love”, “darling,” “honey” etc.; (b) décor features(representation of décor items) such as “heart,” “kiss,” “candle,”“large portions of red,” “loveseat”; (c) preferred colors such as “red”,“pink” etc. and (d) location of images that correspond to the “RomanticStyle” and other information associated with each of the images, such ascolors in an image, features of the image, keywords indicative of thefeatures of the image etc.

The other décor style classifiers “Zen-like Style” and “Abstract” may bedefined similarly. Further, in various embodiments, the décor styleclassifiers may also include information regarding features that are notpreferred by the users for a particular décor style. For example, someusers may not consider a color such as bright green to be romantic. Someusers may not want to have a television in a romantic style room. Theinformation regarding non-preferred features may be included in thedécor style classifiers in various ways. For example, the definition ofdécor style classifiers can have non-preferred data as “exclusions.” Inanother example, each of the keywords or features of the definition maybe associated with a weight that indicates a degree of match of akeyword or feature with a décor style corresponding to the particulardécor style classifier. The décor discovery system 100 ensures that thedécor items having excluded features or features that have a weightbelow a particular threshold are excluded from the recommendation. Invarious embodiments, the definition of the décor style classifiers maybe updated continuously to reflect a change in user preferences.

In some embodiments, a color or color palette could be a separate décorstyle classifier. For example, the décor style classifier “Color_VintageStyle” can describe the colors that match with “vintage” style asperceived by the users.

Referring back to FIG. 2, the system 200 includes a data gatheringmodule 205 that gathers style data from various sources that can be usedto define various décor style classifiers. The data gathering module 205analyzes various sources to identify and extract any information that isrelevant to or associated with décor. The information can include, forexample, descriptions, discussions, or images associated with orrelevant to décor such as artworks, paintings, pictures, artifacts,architectural pieces, arrangement of artworks, color selection, décor ofa room, rugs, mats, furnishings, clothes etc. The information canfurther include descriptions, discussions, or images associated with orrelevant to a style of décor, including a theme of the décor, a mood ofthe person, a type of architecture, color themes etc.

In various embodiments, the data gathering module 205 gathers the abovestyle data in the form of (a) keywords or phrases describing orassociated with the décor style, (b) features associated with the décorstyle, (c) images that correspond to various décor styles, (d) metadataof the images, and (e) location of the images. Examples of keywordsdescribing theme based décor style can include “minimal,” “abstract,”“calm,” “color trends for 2013”; mood based décor style can include“happy,” “party,” “romantic”; architecture type based décor style caninclude “traditional,” “contemporary,” “Victorian”; culture based décorstyle can include “Moroccan,” “Greece,” “Middle Eastern” etc. Examplesof features gathered include color patterns of a décor style, featuresof objects in the décor, arrangement of objects in the décor etc. Invarious embodiments, the data gathering module 205 can use imageanalysis techniques such as morphological analysis to gather featuresfrom the images of various décor items.

The sources from which the data gathering module 205 obtains the aboveinformation can include websites, blogs, journals, documents, magazinesthat are relevant to décor. In some embodiments, a user too can provideinformation regarding décor style to the data gathering module 205.

The style analysis module 210 analyzes the information extracted by thedata gathering module 205 to identify different décor styles, andassociate the keywords, phrases, features, images with theircorresponding décor styles. For example, from the information extracted,the style analysis module 210 may recognize “romantic” as one of thestyles. The style analysis module 210 analyzes the extracted informationto identify the keywords, phrases, features, images with “romantic”décor style. The style analysis module 210 may identify keywords orphrases such as “roses,” “love,” “love is in the air,” “honey,”“darling,” etc. associated with the “romantic” décor style. The styleanalysis module 210 may identify features such as vases with red roses,a love seat, a light colored wall, a number of paintings with subject asromance or love, flower themed chandeliers, large areas of pink color,smooth satin sheets, and cushions with heart designs on them from thesampled images as associated with “romantic” style. The style analysismodule 210 may also analyze metadata associated with sampled images toobtain any information, including keywords indicative of the features ofthe image, that may be used to identify the particular décor style.

Further, in various embodiments, the style analysis module 210 may alsocombine two similar décor styles into one décor style before passing theanalyzed information to the style dictionary creation module 215. Forexample, the style analysis module 210 may combine a particular décorstyle referred to as “love” style by some of the sources with aparticular décor style referred to as “passion” or “fantasy” styles byanother source into one combined style “romance.” In variousembodiments, the keywords, features and images corresponding to theseparate décor styles are also combined into the “romance” style. Insome embodiments, the style analysis module 210 may decide to combinevarious styles into one based on a meaning of keywords of the décorstyles being semantically same.

The style dictionary creation module 215 obtains the analyzedinformation, including décor style and associated keywords, features andimages, from the style analysis module 210, and creates a décor styleclassifier representing the décor style in the décor style dictionary225. An example of the décor style classifier created by the styledictionary creation module 215 can include a décor style classifier suchas the “Romantic Style” illustrated in example implementation 300. Aftercreating the décor style classifier, the style dictionary creationmodule 215 may also store the location of the images corresponding tothe décor style in association with the décor style classifier, asillustrated in the example implementation 300. In various embodiments,the décor style classifier can include, for each of the imagesassociated with the decor style classifier, various details of the imagesuch as colors in the image, keywords associated with image, features ofthe image etc. This information may be useful for creating a variety ofindices for the images that enables fast retrieval based on varioussearch criteria.

In various embodiments, the style dictionary creation module 215 canalso identify other keywords, features and images that can be associatedwith a particular decor style classifier. One example of such anidentification method can include identifying keywords that aresemantically same as the keywords associated the particular décor styleclassifier. The style dictionary creation module 215 can identify suchkeywords from the set of keywords associated with other décor styleclassifiers in the décor style dictionary 225 or can obtain the keywordsfrom a number of other sources such as Internet, English dictionaries,décor magazines etc.

In various embodiments, the décor style dictionary 225 serves as alookup service for identifying a particular décor style classifier basedon the user input of décor style. The particular décor style classifieris then used for generating décor recommendations. The style dictionarycreation module 215 implements the décor style dictionary 225 using asuitable data structure. In various embodiments, the data structure maybe implemented such that it supports fast and efficient searching andretrieval of the décor style classifiers based on the user input. Invarious embodiments, the style dictionary creation module 215 may indexand/or cluster the keywords, features, images based on the décor stylesthey are associated with.

The system 200 includes a recommendation engine 220 that is used tofacilitate recommendation of décor items to the user. The recommendationengine 220 can be used in various ways. For example, the recommendationengine 220 can be used to search various décor items from varioussources and analyze them to determine whether they match with the styledata 115 input by the user. The recommendation engine 220 may also beused to analyze the user input, including style data 115, to identify aparticular décor style classifier from the décor style dictionary.Further, the recommendation engine 220 may also be used to improve theaccuracy of definitions of the decor style classifiers in the décorstyle dictionary 225. In various embodiments, the recommendation engine220 can be implemented using a rule-based expert system (hereinaftersimply “expert system”), a clustering engine, or various otherself-learning techniques that can classify, group, categorize orassociate different data based on a certain criteria. In the context ofthe disclosed technology, the recommendation engine 220 can be used toclassify, group, categorize or associate images and keywordsrepresenting various décors of various décor styles into specific décorstyles.

Consider an example where the recommendation engine 220 is implementedas an expert system. The expert system can include an inference engine(not illustrated) that facilitates in making decisions on recommendationof décor items based on the rules (e.g. décor style definition) definedin a knowledge base (not illustrated). At work, the inference enginereasons about the knowledge base like a human. In various embodiments,the décor style dictionary 225 may form the knowledge base of the expertsystem. Further, the definitions of the décor style classifiers may beexpressed as rules. In various embodiments, the rules are expressed withnatural language such as “IF . . . THEN . . . .” However, the rules maybe formulated in various other ways and may be adapted to a particularinference engine style.

Every rule has an IF part, also called the antecedent and a THEN part,also called the consequent part. These rules must link the evidenceabout the problem under consideration to the conclusion. For example:

-   -   “IF it is love THEN it is romance”    -   “IF style=romance THEN color in décor item≠bright green”    -   “IF the number of décor items in the image is not known with        certainty AND the color pattern includes large portions of pink        AND the morphological features indicate décor items with heart        design AND the sheets on the bed is satin and red THEN there is        a strong probability (0.8) that the style is of type Romance”

The inference engine is designed to produce reasoning on rules. In orderto produce reasoning, it should be based on logic. There are severalkinds of logic, including propositional logic, predicates of order 1 ormore, epistemic logic, modal logic, temporal logic, fuzzy logic,probabilistic logic, etc. Propositional logic is the basic human logicthat is expressed in syllogisms. With logic, the inference engine isable to generate new information from the knowledge contained in theknowledge base and data to be processed. That is, in the example wherethe expert system is used to facilitate recommendation of décor items,the expert system will be able analyze various décor items (e.g. imagesand/or keywords representing the décor items) and classify them, as perthe rules, into appropriate décor styles based on the features and/orkeywords associated with the décor items. The expert system can thenmake improved, informed and more accurate recommendations of decoritems.

Referring back to the recommendation engine 220, consider an examplewhere it is implemented as a clustering engine. The clustering enginecan analyze various décor items (e.g. images and/or keywordsrepresenting the décor items) and create a number of clusters based onthe features and/or keywords associated with the décor items such thatimages having similar features or keywords indicative of similar décorstyle are grouped into a cluster. The cluster represents a particulardécor style. In some embodiments, separate clusters may be formed forfeatures and keywords. Each of the clusters can then be associated witha particular décor style classifier. Such clustering techniques can beused to classify décor items into appropriate décor styles based on thefeatures and/or keywords associated with the décor items and then, makeimproved, informed and more accurate recommendations of decor items.

Further, in various embodiments, the recommendation engine 220 can trackthe user input including style data 115, a list of décors 115recommended by the décor discovery system 105 and the décors chosen ornot chosen by the users for the particular style data 115. Accordingly,the recommendation engine 220 may track the user preferences anddetermine whether a definition of the décor style classifier can beimproved to better match with user's perception of the décor style itrepresents. The recommendation engine 220 may suggest the style creationdictionary module 215 to update the definition of the particular décorstyle classifier accordingly. For example, the recommendation engine 220may suggest updating the definition of a décor style classifier byadding, modifying or removing keywords, features or images associatedwith it. Alternatively or additionally, a user such as an expert in thedécor field may manually train (e.g. add, modify or delete the rules)the recommendation engine 220 to improve the accuracy.

In various embodiments, the recommendation engine 220 may also bedesigned to assign a weight (also referred to as a “style probability”)to the keywords, features or images associated with a décor styleclassifier. The weight indicates a degree of a match between thekeywords, features or images and the corresponding décor styleclassifier. The recommendation engine 220 may adjust (e.g. increase ordecrease) the weight of the keywords, features or images correspondingto the décor style classifier based on the user selection of decors ofthe décor style to which the décor style classifier corresponds. Forexample, if a user selects a particular décor from a plurality of decorsof a décor style to which the décor style classifier corresponds, theexpert system may increase the weight of the keywords and/or featuresrelated to the particular décor in the signature of the décor styleclassifier. The increase in weight indicates an increase in the degreeof match between the keywords and/or features and the décor style towhich the décor style classifier corresponds.

FIG. 4 is an example 400 of images of various décor styles, consistentwith various embodiments. An image 405 can be artwork such as a pictureor a painting. An image 410 can be a picture of a décor of a room. Invarious embodiments, the image can be of other décor items such as aproduct in a catalogue—e.g. sofa, necklace or hat. In the example 400,the images 405 and 410 are considered to be of different styles bydifferent users. For example, while a first user may consider the images405 and 410 to be of “Minimal” and “Eclectic” décor styles,respectively, a second user may consider them to be of “Clean” and“Busy” décor styles, respectively, and a third user may consider them tobe of “Architectural” and “Organic” décor styles, respectively.

A system such as system 200 can analyze the images 405 and 410 andassociate them with particular décor style classifiers. In someembodiments, the system 200 may analyze the keywords describing thedécor styles, features of the images, the color pattern in the images,décor items in the images etc. to determine the décor style classifierthey have to be associated with. In one example, the system 200 maydetermine to associate the image 405 with a décor style classifier, forexample, a “Zen-like” décor style as illustrated in décor styledictionary 300, that has keywords semantically similar to that of“Minimal,” “Clean” and “Architectural”. The system 200 may alsoassociate the foregoing keywords with the décor style classifier“Zen-like”. Additionally or alternatively, the system 200 may associatethe image 405 with another décor style classifier that has features(e.g. morphological features), color pattern etc. similar to that of theimage 405. For example, if an image has areas that lack “patterns”andare linear-edged, uniformly shaped, symmetrical (etc.), then the imagemight be classified as “Minimal” if such a morphology is often seen indécor of “Minimal” style décor.

In some embodiments, the system 200 may associate the image 405 with astyle probability indicating a probability of the image 405 being of aparticular décor style. The system 200 analyzes and associate the image410 with one or more décor style classifiers similarly. Further, in someembodiments, the system 200 also assigns weight to the keywords andfeatures of the images 405 and 410 indicating a degree of match of thekeywords or features to the décor style represented by the décor styleclassifier.

FIG. 5 is an example of a tool 500 for generating a color palette basedon décor style, consistent with various embodiments. The tool 500 can beused to generate a color palette 510 based on décor style 505 input bythe user. The user can choose a palette from the color palette 510 andthen proceed to searching for décor that corresponds with the chosencolor palette and the décor style 505.

In various embodiments, the tool 500 can be implemented in a system suchas system 200 of FIG. 2. The user can input various types of décor stylesuch as décor concept, theme of the décor, mood of a person etc. Thedécor style 505 input by the user includes a mood of a person such as“Whimsical.” The tool 500 also displays a description 515 of the mood“Whimsical.” In some embodiments, the description 515 may help the userto get an understanding of what the tool 500 may interpret the mood as.Upon receiving the décor style 505 input from the user, the tool 500identifies a décor style classifier from the décor style dictionarycorresponding to the décor style 505. The tool 500 then identifies a setof colors associated with the particular décor style classifier andgenerates a color palette 510 with various combinations of colors fromthe set of colors. Further, in some embodiments, the tool 500 may arriveat particular combinations of colors in each color palette 510 based ona weight associated with each of the set of colors.

In some embodiments, the tool 500 identifies a set of colors harmonizedwith a one or more colors input by the user. Given a color preference(e.g. a “key color”) by the user, the tool 500 can generate harmonizedcolors according to various algorithms (e.g. related to color theory).In some embodiments, the set of harmonized colors may not be among thecolors associated with the décor style classifier in the décor styleclassifier dictionary. If the set of harmonized colors are not yetassociated with the décor style classifier in the décor style classifierdictionary, they will be associated with the décor style classifier.

FIG. 5 illustrates the décor style 505 being input as a selection of thedécor styles from a drop down list having predefined values for décorstyles. However, it should be noted that the user may input décor stylesin various other ways, including free text. In some embodiments, the“key” color input by the user can be a selection of a color from apredefined list of colors presented by the tool 500 or could bedescribed by the user using product color names (e.g. from paintcompanies, furniture manufacturers etc.) figurative descriptions (e.g.pale yellow, sunset orange) or any type of descriptions in common use(e.g. as found on the Internet). The system 200 contains a dictionary ofcolor names matched to various instances of color found in varioussources such as product catalogues and descriptions. Given an inputdescription for a color, the system 200 can find the nearest or closestcolor name belonging to a product.

In some embodiments, the user can also input a color using visual means,such as a photograph or a camera. The system 200 can determine aparticular color by applying various known techniques, e.g.probabilistic logic, to guess the color name given the content of theimage (e.g. if the system 200 analyzes that image is a sofa from IKEA,then it can identify the color ranges accordingly). In variousembodiments, such techniques can be used to overcome the oftensignificant limitations of smartphone cameras (e.g. color distortion).

FIG. 6 is a block diagram of a system for discovering décor based ondécor styles, consistent with various embodiments. The system 600 can beimplemented in an environment such as environment 100 of FIG. 1. Thesystem 600 includes a décor request receiving module 610 that receives arequest to search for various décor of a particular décor stylespecified by a user 605. In various embodiments, the user input of thedécor style could be in the form of free text, a user selection ofimages from a list of images representing various decors of certaindécor styles presented to the user 605 by the system 600, a userselection of predefined décor styles, user input of images of décor. Insome embodiments, the free text can include visual features of décor,like “zigzag” or “striped.” Further, the user input can also includedata specifying a type of décor. The type of decor can include one ormore of a décor of a room, including furniture in the room, a colorpattern of the room, clothes, artworks, household items etc.

The expert system 615 determines a number of decors that match with theparticular décor style input by the user 605. In various embodiments,the expert system 615 is similar to the recommendation engine 220 ofFIG. 2. In FIG. 6, although the recommendation engine is implementedusing an expert system 615 other implementations of recommendationengine as described in reference with FIG. 2 are possible. The expertsystem 615 analyzes the user input and identifies a décor styleclassifier from the décor style classifier dictionary database 620 thatcorresponds to the particular décor style specified by the user 605.

In various embodiments, the expert system 615 determines a décor styleclassifier having a signature with features or keywords that match withthe keywords or features of the images of the user input indicative ofthe particular décor style. After identifying the décor styleclassifier, the expert system 615 can obtain the locations of the imagesrepresenting the decors corresponding to the décor style to which thedécor style classifier corresponds from an index in the decor styleclassifier dictionary database 620 and provide them to the décorretrieving module 625. The décor retrieving module 625 retrieves theimages from the corresponding locations and provides them to thepresenting module 645 for presentation to the user 605.

In various embodiments, the expert system 615 can also obtain additionalimages of decors from a number of sources 635. The sources 635 can beaccessed over a communication network 630, such as Internet, local areanetwork (LAN), wide area network (WAN) etc. The sources 635 may or maynot be similar to the sources from which the data gathering module 205obtains the images of décor or the sources the locations of which isindexed in the décor style classifier dictionary database 620. Theexpert system 615 can instruct the decor retrieving module 625 to obtainimages representing various decors of various décor styles from thesources 635. The style analysis module 640 extracts (a) the features and(b) keywords indicative of the features from the additional images andtransmits them to the expert system 615 for further analysis. In variousembodiments, the expert system 615 compares the keywords and thefeatures of the additional images with the keywords and features of thedécor style classifier corresponding to the particular décor stylespecified by the user 605 to determine whether the additional images areharmonized with the particular décor style.

In various embodiments, the expert system 615 can also determine whetheran additional image is harmonized with the particular décor style towhich the décor style classifier corresponds based on a function of theweight of the keywords or the features in the signature of the décorstyle classifier. For example, if the weight of the keywords or thefeatures of décor style classifier that matched with the keywords or thefeatures of an additional image is above a predefined threshold, thenthe expert system 615 may determine that the additional image isharmonized with the particular décor style. On the other hand, if theweight is below the threshold, the expert system 615 may determine thatthe additional image is not of the particular décor style.

Using a threshold for comparing the weight is just one example of afunction of weight. In various embodiments, the expert system 615 canuse various other functions of weight. For example, the expert system615 can use a function that determines an average of the weights of allthe keywords and features of the décor style classifier that match withthe keywords or the features of an additional image. If the averageweight exceeds a predefined threshold, then the additional image isconsidered to be harmonized with the particular décor style. In someembodiments, the average weight of the keywords and features of morethan one décor style classifier that match with the keywords or thefeatures of the additional image may exceed the predefined threshold. Insuch a case the additional image may be considered to be harmonized withmore than one décor style.

The presentation module 645 presents the images of decors to the user605. In various embodiments, the presentation module 645 can present theimages of the décors using a three dimensional (3D) computer generatedimagery (CGI) modeling. In various embodiments, the 3D CGI modelinggenerates photo realistic imagery of the décor. For example, a 3D CGImodeling of a décor such as a décor of a room generates a photorealistic imagery of the room containing various décor items. In variousembodiments, the user 605 can further customize the décor of the room byadding, modifying or removing décor items from the room, and view the 3DCGI of the customized decor. Further, the user may dynamically customizethe décor in the 3D CGI of the room. For example, the user 605 maychange the color of a portion or the whole room, move an artwork fromone position to another, or remove a couch from the room, change thegeometrical data such as size of the wall that affects the overallconfiguration of the room, and simultaneously view the 3D CGI of thecustomized décor of the room.

FIG. 7 is an example 700 of a presentation of an artwork 705 in a roomusing 3D CGI modeling, consistent with various embodiments. The artwork705 can be viewed in 3D CGI of such various rooms.

Referring back to FIG. 6, in various embodiments, the system 600 candiscover various décor items that are harmonized with a décor style of aspecific room input by the user 605. In various embodiments, the user605 may upload a two dimensional (2D) image of the specific room. Forexample, the user 605 may include as user input to the décor requestreceiving module 610 an image of the décor of a room the user 605intends to find the décor items for. The expert system 615 determinesthe features of the room, e.g. using on morphological analysis, findsthe décor items (e.g. as explained above) and presents the décor itemsto the presentation module 645. The presentation module 645 can generatea 3D CGI of the room input by the user 605 with décor items discoveredby the expert system 615.

In various embodiments, the system 600 can also generate descriptionssuch as a reason why a particular décor was chosen by the system 600.For example, an artwork can be presented with an art descriptor thatincludes a brief description of the artwork and/or a reason why theartwork is selected. In various embodiments, the art descriptor may helpthe user understand why a particular artwork was recommended.

Consider a scenario where the user 605 intends to obtain décor, such asartworks, based on a specific user-input color palette. The expertsystem 615 can find and present artworks that includes one or morecolors in addition to or instead of the colors specified in theuser-input color palette. In various embodiments, the expert system 615may select such artworks because it may consider that the one or morecolors harmonizes well with the specific user-input color palette. Thepresentation module 645 presents the art descriptor in association withthe artworks. FIG. 8 is an example 800 of presenting an art descriptor810 in association with an artwork 805, consistent with variousembodiments. The art descriptor 810 can include a brief description suchas a name of the artwork and artist. The art descriptor 810 can alsoinclude a reason for selection of the artwork 805, such as “Red DaisyFlower was curated for you because it contains a rich expression ofCandy Floss, a faint wisp of Fandango and a delicate dash ofCappuccino.” The art descriptor 810 can also include a reason for whythe artwork 805 with the colors “Candy Floss,” “Fandango” and“Cappuccino” was selected even though the user did not include thosecolors in the specific user-input color palette (not illustrated). Thereason can be, for example, “These colors are congruent with your loveof Get Reddy, Harlequin and Brandy” as illustrated in art descriptor810.

In various embodiments, the presentation module 645 can also highlightthe text indicative of the colors in the art descriptor 810. Forexample, a font or a background of a text indicative of a particularcolor may be presented in the particular color. In the example 800, theart descriptor 810 presents a text indicative of a particular color,e.g. “Candy Floss” with the “Candy Floss” color background. In variousembodiments, the presentation module 645 can also present a colorpalette 815 of the artwork 805 in association with the artwork 805.

Referring back to FIG. 6, the system 600 can also generaterecommendations regarding various possible arrangements of décor itemsin a décor. For example, the system 600 can generate recommendationsregarding various arrangements of décor such as artworks based on one ormore of features such as a color palette of the artworks, a frame typeof the artworks, features of the artworks, a finish type, e.g. matte,glossy, etc. of the artworks, a genre, an artist, content of theartworks.

In various embodiments, the system 600 can also determine a text, thatis a description e.g. magazines, online articles etc., representing theplurality of decors harmonized based on the décor style classifier. Insome embodiments, the system 600 can also determine applications (e.g.online applications) for creating a plurality of decors harmonized basedon the décor style classifier.

FIGS. 9A, 9B and 9C collectively is an example of various arrangementsof a set of artworks, consistent with various embodiments. The variousarrangements of the artworks are generated using a system such as system600 of FIG. 6. The example arrangements 900, 950 and 975 of FIGS. 9A, 9Band 9C, respectively, illustrate arrangements of six artworks based ondifferent criteria. The example of FIG. 9A, illustrates an arrangement900 of six pieces of “abstract” style artwork with a color palette ofgreen, blue and red predominantly distributed among the artworks. Invarious embodiments, one of the reasons for recommending variousarrangements is to create an arrangement that is sophisticated orpleasing to the eye and in congruence with the décor style. Thearrangement 900 is one such arrangement where the artworks are arrangedin a specific way based on the color palette of the artworks. In thearrangement 900, in the first row, the artworks with predominantly greenpalette are alternated with a predominantly blue palette artwork, and ina second row, the artworks with predominantly blue palette arealternated with a predominantly red palette artwork. In variousembodiments, the artworks may be arranged in various other patterns.Additionally or alternatively, the arrangement can also be based on acolor pattern in the artwork, that is, a pattern of one or more colorsin the artwork. Further, in various embodiments, the expert system 615may provide improved recommendations of arrangements based on a userselection/input/feedback on prior recommendations.

The example of FIG. 9B illustrates an arrangement 950 of six pieces ofartwork having a color palette distributed among green, blue and red. Inthe example of FIG. 9B, each of the artworks have an equal distributionof at least two colors. Further, a primary color, e.g. green, is in atop portion of all the artworks and secondary and tertiary colors, blueand red, are in bottom portions of three pieces of artworks each. Thearrangement 950 includes, in the first row, artworks with shades ofgreen in the top portion and blue in the bottom portion and, in thesecond row, artworks with green in the top portion and red in the bottomportion. In various embodiments, the system 600 may also consider acolor of the frame, a finish type of the artwork, etc. in recommendingvarious arrangements. In general, the images can be arranged accordingto their color, size and style to obtain an arrangement that iscongruent with the user's taste and/or the décor style of the room.

In the example FIG. 9C, artworks are arranged based on features such asmorphological features of the images. The example of FIG. 9C includes acombination of artworks that are considered either predominantly busy,partly busy, or not busy. For example, the first and third artworks inthe first row of arrangement 975 can be considered to be ofpredominantly “busy” style. Similarly, the first artwork in the secondrow is considered to be partly busy on the right portion of the artwork,and the third artwork is considered to be partly busy on the leftportion of the artwork. The arrangement 975 places, in the first row,the predominantly busy artworks separated by a not busy blue colorpalette artwork. In the second row, the arrangement 975 places thepartly busy artworks separated by a not busy red color palette artwork.The arrangement 975 has placed the partly busy artworks in a symmetricalfashion, e.g. symmetrical on either sides of the red artwork. Variousother combinations are possible. For example, the second row may bearranged as the first row or the partly busy artworks may be placed suchthat the busy portions are facing away from one another instead oftowards each other.

In various embodiments, the recommendation of arrangement combinationscan also consider factors such as a layout of the arrangement, e.g.landscape, portrait, a circular arrangement etc.; a dimension of theavailable space where the artworks are intended to be installed; a shapeof the frame of the artworks; existing decor items, e.g. vases or softfurnishings of a certain pattern, design and color etc., already in aroom where the artworks are intended to be installed.

In various embodiments, similar recommendation, including what décoritems to buy in what color, pattern and design to create a particulardécor arrangement, such as vases, rugs, cushions and art, can beprovided. For example, the striped pattern of cushions could be deployedas a motif for the wall décor, in unison, contra-unison etc. with aparticular décor style.

In some embodiments, the system 600 can also recommend fashionaccessories and/or clothes based on a particular décor style specifiedby the user. For example, the user may want a recommendation of clothingaccessories based on a particular style of a celebrity such as “MichaelJackson.” The system 600 discovers various décor items such as hats,ties, shoes, glove etc. that users typically associate with MichaelJackson, that are endorsed, worn, are similar to that worn by MichaelJackson, etc. In another example, the system 600 can also providerecommendations of fashion accessories based on a particular fashiondesigner, a particular color trend. In some embodiments, the system 600can also recommend fashion accessories that match, complement,supplement, or contrast with a particular accessory input by the user(as an image and/or keywords describing the particular accessory) basedon one or more of color, design pattern, size, designer of theparticular color accessory etc.

Referring back to FIG. 6, in various embodiments, the system 600 canalso present recommendations regarding service providers who canfacilitate creating the decors discovered by the system 600 and/ormerchants where the decors are available for purchase. The serviceproviders or the merchants can include clothing designers, jewelers,interior decorators, architects, décor experts, websites that includeinformation on décor styles, online applications that can help createdécor based on the décor styles etc. In various embodiments, the serviceproviders or the merchants can have affiliation with a provider ofsystem 600 to facilitate selling of their services or productsassociated with décor.

FIG. 10 is a flow diagram of a process for discovering décor based ondécor styles, consistent with various embodiments. In variousembodiments, the process 1000 may be executed in a system such as system600 of FIG. 6. At step 1005, the décor request receiving module 610receives a request to search for images of décor based on a particulardécor style input by a user. In various embodiments, the user input ofdécor style could be in the form of free text, user selection of images,user selection of predefined décor styles, including tastes ofwell-known designers etc. For example, the user may want to decoratetheir room according to the style and tastes of an expert (or acelebrity) whose style and taste can be analyzed using the methodsalready described above. In various embodiments, the styles of userssuch as experts, celebrities can be created as separate décor stylesusing the methods described above or the décor styles in the décor styleclassifier dictionary that match with the styles of the users orcelebrities can be tagged with certain stylists, designers etc.

At step 1010, the expert system 615 analyzes the user input and performsa lookup in a décor style classifier dictionary database 620 to identifya décor style classifier that corresponds to the décor style input bythe user. The décor style classifier dictionary 620 includes a number ofdécor style classifiers that correspond to various décor styles. Invarious embodiments, the analysis of the user input can includecomparing the keywords and/or features of images of the user input withthe keywords and/or features in the décor style classifier dictionary toidentify a matching décor style classifier. If the expert system 615does not find a match, it may present a few keywords from the décorstyle classifier dictionary that is probably relevant to or issemantically similar to the keywords of the user input to the user. Theuser may then select one or more of the presented keywords.

At step 1015, the expert system 615 identifies the décor styleclassifier based on the analysis of step 1010. At step 1020, the expertsystem 615 determines whether the décor request receiving module 610indicated that a user wants to choose a color palette. At step 1025,responsive to a determination that the user wants to choose a colorpalette, the expert system 615 generates a number of color palettescorresponding to the décor style classifier, e.g. based on the colorsincluded in the signature of the décor style classifier for selection bythe user. At step 1030, the expert system 615 determines various decorsthat are harmonized with the user selected color palette and the décorstyle to which the décor style classifier corresponds. In an embodiment,the expert system 615 refers to the index of images, which is createdbased on the décor style classifiers, in the décor style classifierdictionary database 620 to obtain the location of images matching withthe selected color palette and the décor style to which the décor styleclassifier corresponds. Then the expert system 615 can request the decorretrieving module 625 to obtain those images from the correspondinglocations. At step 1040, the presentation module 645 presents the imagesof the harmonized décor.

Referring back to step 1020, if the expert system 615 determines thatthe user does not want to choose a color palette, at step 1035, theexpert system 615 determines various decors that are harmonized with thedécor style to which the décor style classifier corresponds. At step1040, the presentation module 645 presents the images of the harmonizeddécor.

FIG. 11 is a flow diagram of a process for creating a décor styledictionary, consistent with various embodiments. In various embodiments,the process 1100 may be executed in a system such as system 200 of FIG.2. At step 1105, the data gathering module 205, obtains content such asa number of images and description indicative of various décor stylesfrom a number of sources. At step 1110, the style analysis module 210determines a signature of a particular décor style which includes anumber of features of an image and keywords indicative of the particulardécor style. At step 1115, the style dictionary creation module 215,determines whether the décor style dictionary includes a décor styleclassifier corresponding to the particular décor style. If the décorstyle dictionary includes a décor style classifier corresponding to theparticular décor style, at step 1120, the style dictionary creationmodule 215 updates the décor style classifier to include the featuresand the keywords in addition to existing features and keywordsassociated with the décor style classifier.

On the other hand, if the décor style dictionary does not include adécor style classifier corresponding to the particular décor style, atstep 1125 the style dictionary creation module 215 adds a new décorstyle classifier corresponding to the particular style to the décorstyle classifier dictionary and associates the signature of theparticular décor style with the new décor style classifier.

FIG. 12 is a flow diagram of a process for associating décor styles to adécor style classifier, consistent with various embodiments. In variousembodiments, the process 1200 may be executed in a system such as system200 of FIG. 2. At step 1205, the recommendation engine 220 (alsoreferred as expert system 220) determines a set of features and a set ofkeywords indicative of a particular décor style to be associated with aparticular décor style classifier. In various embodiments, the expertsystem 220 is a rules-based expert system. The expert system 220includes data regarding the décor style classifiers and a number offeatures and keywords associated with each of the décor styleclassifiers, as a number of rules.

At step 1210, the expert system 220 associates a particular décor styleclassifier in the décor style classifier dictionary with the set offeatures and the set of keywords determined based on the rules. At step1215, the expert system 220 tracks style preference data of a number ofusers for a décor style to which a particular décor style classifiercorresponds. At step 1220, the expert system 220 updates the set offeatures and/or keywords associated with the particular décor styleclassifier based on the style preference data. Also, in variousembodiments, the expert system 220 associates a weight with each of theset of features and keywords that indicates a degree of match of aparticular feature or keyword with the particular décor style.

FIG. 13 is a flow diagram of a process for creating a décor of aparticular décor style, consistent with various embodiments. In someembodiments, the process 1300 may be executed in association with asystem such as system 600 of FIG. 6. At step 1305, the décor requestreceiving module 610 receives from a user data specifying a particulardécor type and a particular décor style of the décor that the user isinterested in creating. In some embodiments, the user can specify thedata, including the particular décor style and type, using a set ofkeywords. For example, data specifying a particular décor type caninclude keywords “décor of a room,” and the data specifying a particulardécor style can include keywords “romantic.” However, the user can inputdata in various other forms, including as a selection of predefinedstyles and/or décor types.

At step 1310, the expert system 615 analyzes the data to identify adécor style classifier from a décor style classifier dictionary databasewhose signature matches with the particular décor style. As describedwith reference to at least FIGS. 2 and 3, a signature of a décor styleclassifier can include a plurality of features representative of thedécor of the particular décor style to which the décor style classifiercorresponds. The signature can also include, e.g., as “exclusions,” aplurality of features that are to be excluded from the décor in orderfor the décor to conform to the particular décor style.

At step 1315, presentation module 645 obtains the recommendations fromthe expert system 615 and presents the recommendations for creating thedécor of the particular décor style and décor type to the user based onthe signature of the décor style classifier. In some embodiments, therecommendations can include features representative of the décor of theparticular décor style and the particular décor type to which the décorstyle classifier corresponds. As described above, the features of décorcan include one or more of (a) décor items in the décor, (b) a placementof the décor items, (c) color pattern of the décor, (d) color of thedécor items, or (e) design of the décor items, e.g. shape, form,material, pattern etc. In some embodiments, such features may bedetermined using morphological analysis of one or more imagesrepresenting a plurality of decors of the particular décor style and theparticular décor type.

Referring back to step 1315, in the example of creating a “décor of aroom” with “romantic” style, the recommendations can include, “Considerincluding a bed in the shape of heart.” It can also include “Considerhaving a white bed sheet made of silk or satin.” It can also include“Consider having a number of candles by the bed.” It can also include“Consider having a combination of artworks, including paintings, muralsor sculptures depicting love.” It can also include “Consider havingvases with red roses.” It can also include “Consider having vases thatare of soft or light colors.”

In some embodiments, the recommendations can also include a set of thefeatures that the user can consider excluding from the décor in orderfor the décor to conform to the particular décor style. Continuing withthe above example, the recommendation can include “Do not include atelevision set in the room.” It can also include “Consider removing anybright or dark décor items.” It can also include “Do not include anyloud paintings.”

Further, the recommendations can be presented as (a) images of décorcreated based on the recommendations and/or (b) a plurality of wordsdescribing each of the recommendations.

FIG. 14 is a block diagram of a computer system as may be used toimplement features of some embodiments of the disclosed technology. Thecomputing system 1400 may include one or more central processing units(“processors”) 1405, memory 1410, input/output devices 1425 (e.g.,keyboard and pointing devices, display devices), storage devices 1420(e.g., disk drives), and network adapters 1430 (e.g., networkinterfaces) that are connected to an interconnect 1415. The interconnect1415 is illustrated as an abstraction that represents any one or moreseparate physical buses, point to point connections, or both connectedby appropriate bridges, adapters, or controllers. The interconnect 1415,therefore, may include, for example, a system bus, a PeripheralComponent Interconnect (PCI) bus or PCI-Express bus, a HyperTransport orindustry standard architecture (ISA) bus, a small computer systeminterface (SCSI) bus, a universal serial bus (USB), IIC (I2C) bus, or anInstitute of Electrical and Electronics Engineers (IEEE) standard 1394bus, also called “Firewire”.

The memory 1410 and storage devices 1420 are computer-readable storagemedia that may store instructions that implement at least portions ofthe described technology. In addition, the data structures and messagestructures may be stored or transmitted via a data transmission medium,such as a signal on a communications link. Various communications linksmay be used, such as the Internet, a local area network, a wide areanetwork, or a point-to-point dial-up connection. Thus, computer-readablemedia can include computer-readable storage media (e.g.,“non-transitory” media) and computer-readable transmission media.

The instructions stored in memory 1410 can be implemented as softwareand/or firmware to program the processor(s) 1405 to carry out actionsdescribed above. In some embodiments, such software or firmware may beinitially provided to the processing system 1400 by downloading it froma remote system through the computing system 1400 (e.g., via networkadapter 1430).

The technology introduced herein can be implemented by, for example,programmable circuitry (e.g., one or more microprocessors) programmedwith software and/or firmware, or entirely in special-purpose hardwired(non-programmable) circuitry, or in a combination of such forms.Special-purpose hardwired circuitry may be in the form of, for example,one or more ASICs, PLDs, FPGAs, etc.

Remarks

The above description and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known details are not described in order to avoidobscuring the description. Further, various modifications may be madewithout deviating from the scope of the invention. For example, thoughthe embodiments illustrate discovering décor such as artworks, décor ofa room, the decor is not limited to artworks or décor of a room. Thedécor can include one or more of décor items such as paintings,pictures, artifacts, sound, architectural pieces, arrangement ofartworks, color selection, rugs, mats, furnishings, clothes, jewelry,fashion, car interiors, flower arrangement, garden etc. Accordingly, theinvention is not limited except as by the appended claims.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not for other embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. For convenience, certainterms may be highlighted, for example using italics and/or quotationmarks. The use of highlighting has no influence on the scope and meaningof a term; the scope and meaning of a term is the same, in the samecontext, whether or not it is highlighted. It will be appreciated thatthe same thing can be said in more than one way. One will recognize that“memory” is one form of a “storage” and that the terms may on occasionbe used interchangeably.

Consequently, alternative language and synonyms may be used for any oneor more of the terms discussed herein, nor is any special significanceto be placed upon whether or not a term is elaborated or discussedherein. Synonyms for certain terms are provided. A recital of one ormore synonyms does not exclude the use of other synonyms. The use ofexamples anywhere in this specification including examples of any termdiscussed herein is illustrative only, and is not intended to furtherlimit the scope and meaning of the disclosure or of any exemplifiedterm. Likewise, the disclosure is not limited to various embodimentsgiven in this specification.

Those skilled in the art will appreciate that the logic illustrated ineach of the flow diagrams discussed above, may be altered in variousways. For example, the order of the logic may be rearranged, substepsmay be performed in parallel, illustrated logic may be omitted; otherlogic may be included, etc.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this disclosure pertains. In the case of conflict, thepresent document, including definitions will control.

What is claimed is:
 1. A method for providing and managing a décor styleclassifier dictionary database, the method comprising: receiving, at acomputer system, content from a first plurality of sources, the contentincluding (a) a first plurality of images of a plurality of décors, theplurality of decors being of a plurality of décor styles, and (b) adescription indicative of the decor styles; determining, by the computersystem, a signature of a particular décor style from the plurality ofdécor styles, the signature including (a) representations of a firstplurality of features of an image of the images, the image correspondingto the particular décor style and (b) a first plurality of keywordsindicative of the particular décor style; and generating, by thecomputer system, a décor style classifier in the décor style classifierdictionary database, the décor style classifier dictionary databaseincluding a plurality of décor style classifiers corresponding to thedécor styles, the generating including associating the signature withthe décor style classifier.
 2. The method of claim 1, whereindetermining the signature including representations of the firstplurality of features of the image includes determining morphologicalfeatures of the image.
 3. The method of claim 1, wherein determining thesignature including representations of the first plurality of featuresof the image includes determining a color pattern of the image.
 4. Themethod of claim 1, wherein determining the first plurality of keywordsdescribing the particular décor style further includes determiningadditional keywords corresponding to a metadata of the image thatdescribes the particular décor style, and associating the additionalkeywords with the décor style classifier.
 5. The method of claim 1,wherein determining the signature including first plurality of keywordsdescribing the particular décor style further includes determining, froma second plurality of sources, additional keywords that are semanticallysame as the first plurality of keywords, and associating the additionalkeywords with the décor style classifier.
 6. The method of claim 1further comprising: determining, using a recommendation engine, whetherto associate the décor style classifier with a set of features and a setof keywords indicative of the particular décor style, and associatingthe décor style classifier with the set of features and the set ofkeywords based on the recommendation engine.
 7. The method of claim 6,wherein the recommendation engine includes a rules-based expert system,the rules-based expert system including a plurality of rules fordetermining the décor style classifiers based on a second plurality offeatures of a second plurality of images representing the plurality ofdecors of the plurality of décor styles and a second plurality ofkeywords indicative of the plurality of décor styles, the set offeatures and the set of keywords being a subset of the second pluralityof features and the second plurality of keywords, respectively, andwherein the associating includes associating the décor style classifierwith the set of features and the set of keywords based on the pluralityof rules.
 8. The method of claim 7, wherein associating the set offeatures and the set of keywords with the décor style classifier usingrules-based expert system further includes tracking, by the rules-basedexpert system, style preference data of a plurality of users for thedécor style classifier, and updating, by the rules-based expert system,the set of features and the set of keywords associated with the décorstyle classifier based on the style preference data, the updatingincluding associating a weight with each feature of the set of featuresand each keyword of the set of keywords that indicates a degree of matchof a particular feature or keyword with the particular décor style. 9.The method of claim 6, wherein the recommendation engine includes aclustering engine to create a first set of clusters based on a secondplurality of features of a second plurality of images representing theplurality of decors of the plurality of décor styles, each cluster ofthe first set of clusters including a subset of the second plurality offeatures representing a specific décor style, create a second set ofclusters based on a second plurality of keywords indicative of theplurality of décor styles, each cluster of the second set of clustersincluding a subset of the second plurality of keywords representing aspecific décor style, associate a first cluster from the first set ofclusters that has the set of features with the décor style classifier,and associate a second cluster from the second set of clusters that hasthe set of keywords with the décor style classifier.
 10. The method ofclaim 1 further comprising: indexing the first plurality of images andthe first plurality of keywords from the analyzed content based on thedécor style classifiers they are associated with; and assigning styleprobabilities to the first plurality of images, the style probabilitiesindicating a probability of a particular image belonging to the décorstyles corresponding to the décor style classifiers.
 11. The method ofclaim 1 further comprising: receiving, at the computer system and from auser, user input including data indicative of a required décor style,the user input including at least one of a second image or a secondplurality of keywords describing the required décor style; analyzing, atthe computer system, the user input to identify a target décor styleclassifier from the décor style classifier dictionary database thatcorresponds to at least one of the second image or the second pluralityof keywords; and identifying the target décor style classifier based onthe analysis.
 12. The method of claim 11, wherein analyzing the userinput includes at least one of (a) comparing the second plurality ofkeywords of the user input with the first plurality of keywords from thedécor style classifier dictionary database or (b) comparing a pluralityof features of the second image with the first plurality of featuresfrom the décor style classifier dictionary.
 13. The method of claim 12,wherein the comparing the second plurality of keywords of the user inputwith the first plurality of keywords from the décor style classifierdictionary database includes determining whether there is match betweenthe second plurality of keywords and the first plurality of keywords,responsive to a determination that there is no match, presenting, to theuser, a subset of the first plurality of keywords from the décor styleclassifier dictionary database that is probably relevant to or issemantically similar to the second plurality of keywords of the userinput, receiving a user selection of the subset of the first pluralityof keywords, and identifying the décor style classifier corresponding tothe user selection of the subset of the first plurality of keywords. 14.The method of claim 11 further comprising: generating, in response to arequest from the user to generate a plurality of color palettescorresponding to the target décor style classifier, the color palettes,wherein the color palettes is generated based on color patternsassociated with target décor style classifier.
 15. The method of claim1, wherein the décor style includes at least one of (a) a concept of thedécor, (b) a category of the décor, or (c) a mood associated with décor.16. A method comprising: receiving, at a computer system, a request forgenerating a plurality of color palettes for a décor, the requestincluding user data indicative of a décor style; analyzing, by thecomputer system, the user data to identify a décor style classifier froma décor style classifier dictionary database containing a plurality ofdécor style classifiers corresponding to a plurality of décor styles,the décor style classifier corresponding to the décor style; andgenerating, by the computer system, the color palettes corresponding tothe décor style classifier.
 17. The method of claim 16, wherein the userdata indicative of the décor style includes a plurality or keywords orphrases.
 18. The method of claim 17, wherein analyzing the user data toidentify the décor style classifier from a décor style classifierdictionary database includes comparing the keywords or phrases from theuser data with a set of keywords or phrases in the décor styleclassifier dictionary database, and identifying the décor styleclassifier corresponding to the set of keywords or phrases in the décorstyle classifier dictionary database that match with the keywords orphrases from the user data.
 19. The method of claim 18, whereincomparing the keywords or phrases from the user data with the set ofkeywords or phrases in the décor style classifier dictionary includescomparing the keywords or phrases from the user data with the set ofkeywords or phrases in the décor style classifier dictionary databasesemantically.
 20. The method of claim 16, wherein identifying the décorstyle classifier from a décor style classifier dictionary includescreating the décor style classifier dictionary database, the creatingincluding receiving, by the computer system, content from a firstplurality of sources, the content including (a) a first plurality ofimages corresponding to a plurality of décors of a plurality of décorstyles, and (b) a description indicative of the styles of the décor,determining, by the computer system, a signature of a particular décorstyle, the signature including (a) a first plurality of colors of animage of the images, the image corresponding to the particular décorstyle and (b) a first plurality of keywords indicative of the particulardécor style, and adding a new décor style classifier corresponding tothe particular décor style to the décor style classifier dictionarydatabase and associating the signature with the new décor styleclassifier.
 21. The method of claim 20, wherein adding the new décorstyle classifier includes determining whether the décor style classifierdictionary database includes the new décor style classifiercorresponding to the particular style, and responsive to thedetermination that the décor style classifier dictionary databaseincludes the new décor style classifier, updating the new décor styleclassifier to include the first plurality of colors and the firstplurality of keywords in addition to existing plurality of colors andkeywords associated with the new décor style classifier.
 22. The methodof claim 21 further comprising: determining a set of colors to beassociated with a particular décor style classifier using a rules-basedexpert system, the rules-based expert system including data regardingthe décor style classifiers and a plurality of colors associated witheach of the décor style classifiers as a plurality of rules, the set ofcolors indicative of a style of the décor corresponding to theparticular décor style classifier, the set of colors being a subset ofthe plurality of colors; and associating the particular décor styleclassifier in the dictionary database with the set of colors based onthe plurality of rules.
 23. The method of claim 22, wherein associatingthe set of colors with the particular décor style classifier usingrules-based expert system further includes tracking, by the rules-basedexpert system, color preference data of a plurality of users for theparticular décor style classifier, and updating, by the rules-basedexpert system, the set of colors associated with the particular décorstyle classifier based on the color preference data, the updatingincluding associating a weight with each of the set of colors thatindicates a degree of match of a particular color with the particularstyle.
 24. A non-transitory computer-readable medium storing programinstructions that, when executed by a processor, causes the processor toperform the method of receiving, at a computer system, content from afirst plurality of sources, the content including (a) a first pluralityof images of a plurality of décors, the first plurality of decors beingof a plurality of décor styles, and (b) a description indicative of thedecor styles; determining, by the computer system, a signature of aparticular décor style from the plurality of décor styles, the signatureincluding (a) a first plurality of features of an image of the images,the image corresponding to the particular décor style and (b) a firstplurality of keywords indicative of the particular décor style; andgenerating, by the computer system, a décor style classifier in a décorstyle classifier dictionary database, the décor style classifierdictionary database including a plurality of décor style classifierscorresponding to the décor styles, the generating including associatingthe signature with the décor style classifier.
 25. An apparatus forproviding and managing a décor style classifier dictionary database, theapparatus comprising: a processor; a style dictionary creation modulethat works in co-operation with the processor to create a décor styledictionary database containing a plurality of décor style classifiersthat correspond to a plurality of décor styles, each of the plurality ofdécor style classifiers associated with a signature defining aparticular décor style of the plurality of décor styles; a styleanalysis module that works in co-operation with the processor todetermine the signature of the particular décor style, the signatureincluding (a) representations of a plurality of features of an imagecorresponding to the particular décor style and (b) a plurality ofkeywords indicative of the particular décor style; and an expert systemthat works in co-operation with the processor to track style preferencedata of a plurality of users for each of the décor style classifiers andassociate a weight with each of the plurality of features and each ofthe plurality of keywords of each of the décor style classifiers, theweight indicating a degree of match of a particular feature or keywordwith the particular décor style.
 26. A method for creating a décor of aparticular décor style, the method comprising: receiving, at a computersystem and from a user, data specifying a particular décor type of thedécor and the particular décor style that are used for creating thedécor; analyzing, at the computer system, the data to identify a décorstyle classifier from a décor style classifier dictionary database whosesignature matches with the particular décor style, the signatureincluding at least one of (a) a first plurality of featuresrepresentative of the décor of the particular décor style and theparticular décor type or (b) a second plurality of features that are tobe excluded from the décor for the décor to conform to the particulardécor style, the décor style classifier dictionary database including aplurality of décor style classifiers corresponding to a plurality ofdécor styles; and presenting, by the computer system and to the user,recommendations regarding creating the décor of the particular décortype and the particular décor style, the recommendations including atleast one of (a) at least a subset of the first plurality of features tobe considered for creating the décor or (b) at least a subset of thesecond plurality of features to be excluded from the décor.
 27. Themethod of claim 26, wherein the first plurality of features includes atleast one of (a) décor items in the décor, (b) a placement of the décoritems, (c) color pattern of the décor, (d) color of the décor items, or(e) design of the décor items.
 28. The method of claim 27, wherein thefirst plurality of features are determined using morphological analysisof one or more images representing a plurality of decors of theparticular décor style and the particular décor type.
 29. The method ofclaim 26, wherein the particular décor type includes at least one of (a)home décor, (b) fashion, (c) artworks, (d) paintings, (e) clothes, (f)jewelry, (g) car interiors, or (h) flower arrangement.
 30. The method ofclaim 26, wherein the recommendations are presented as at least one of(a) a plurality of images, each of the images representative of thedécor based on one of the recommendations or (b) a plurality of wordsdescribing each of the recommendations.